Aggregating For Expectations

After seeing the performance of the method that decides when to enter and exit general market positions using the percentage of stock with prices over a 200 day moving average I decided to test other similar aggregating measures, mainly RSI, ROC and Momentum. MOM so far has turned out to be the best of the 3.

What

First and foremost, this is an unproven method of choosing when to enter and exit a position. Since back-testing is used to produce the models future results may not reflect or produce expected returns.

2015-03-02

Based on the running total from the beginning of this year I can see this strategy does not work. Therefore I will stop publishing this concept.

At run time 3/2/15 3:02 PM the paper account value is $99001.06 with a starting investment of $100000.
Model Run

Here's The Paper Trail

The Process

This model creates a short to medium term indicator - generally one to four weeks.

We select several leveraged ETFs that best represent the overall market or industry they represent.

For each ETF the process finds the best 50 or so stocks that correlate to the ETF's price movement. Then it finds the average Momentum indicator value for all 50 over 3, 4 and 5 day periods. We accumulate all possible cross-over values or buy/sell points.

Finally a series of back-tests are run that determine the best choice of each period and buy/sell points that occur over the past year.

These best choices are then used daily to calculate current buy/sell points for each ETF using data from the past 6 months.